Understand the models, not just the code
Data science is where statistics, programming and real-world questions meet. Learn to clean messy data, find patterns, build and evaluate machine learning models, and explain your results clearly, with a tutor who makes the maths make sense.
Why learn machine learning one-to-one?
Machine learning courses tend to fail people in one of two ways. Some drown learners in mathematics before they have seen why it matters. Others skip the maths entirely, leaving learners able to run code but unable to tell when a model is wrong. One-to-one teaching lets us find the right balance for you: enough statistics and linear algebra to understand what models do, introduced exactly when you need it.
Projects use real public datasets, from cricket statistics to air quality, house prices and health data, so you practise the most time-consuming part of real data science: cleaning and understanding data before modelling it.
Course roadmap
Python for data
Notebooks, NumPy and pandas for loading, cleaning and reshaping data.
Exploring and visualising
Charts that reveal patterns, and how to avoid misleading ones.
Statistics you will actually use
Distributions, sampling, correlation, hypothesis tests and confidence intervals.
Supervised learning
Regression and classification with scikit-learn: linear models, trees, random forests and gradient boosting.
Evaluating models
Train/test splits, cross-validation, accuracy versus precision and recall, overfitting.
Unsupervised learning
Clustering and dimensionality reduction for finding structure.
Neural network foundations
How networks learn, and a first deep learning model.
Communicating results
Turning analysis into a clear report or dashboard for non-technical readers.
Portfolio projects
Prediction model
A model predicting prices, results or demand, with an honest evaluation.
Classification project
Detecting spam, churn or risk, with a discussion of errors and fairness.
End-to-end case study
A question, a dataset, an analysis and a written report you can share with employers.
Machine learning questions
How much maths do I need?
School-level algebra is enough to start. We teach the statistics and the ideas behind linear algebra and calculus as the models need them.
Is this suitable alongside A-Level or FSc?
Yes, for students comfortable with Python basics. It pairs well with A-Level Maths, Statistics, Computer Science or Economics.
What is the difference between data analytics and data science?
Data analytics focuses on describing what happened using Excel, SQL and dashboards. Data science adds prediction and machine learning with Python. Many learners start with analytics.
Related courses and guides
Book a free data science demo
Tell us who the classes are for and what you want to learn. A real person from our admissions team replies on WhatsApp, usually within a few hours, and books your free demo.
- Free first session, no card needed
- Skills check and a written learning plan
- Matched with a specialist tutor within 24 hours
- Prefer to talk? Message us on WhatsApp